I am a third-year a Ph.D. student in Actuarial Science at the University of Illinois Urbana-Champaign, advised by Prof. Zhiyu Quan. Prior to joining the doctoral program, I obtained a Master’s degree in Actuarial Science at the University of Illinois and a Bachelor’s degree in Mathematics at Nankai University. My research interests lie in machine learning applications in actuarial science. I have been a graduate supervisor of several InsurTech-related research projects in IRisk Lab since Spring 2021.
selected publications
Imbalanced learning for insurance using modified loss functions in tree-based models
Tree-based models have gained momentum in insurance claim loss modeling; however, the point mass at zero and the heavy tail of insurance loss distribution pose the challenge to apply conventional methods directly to claim loss modeling. With a simple illustrative dataset, we first demonstrate how the traditional tree-based algorithm’s splitting function fails to cope with a large proportion of data with zero responses. To address the imbalance issue presented in such loss modeling, this paper aims to modify the traditional splitting function of Classification and Regression Tree (CART). In particular, we propose two novel modified loss functions, namely, the weighted sum of squared error and the sum of squared Canberra error. These modified loss functions impose a significant penalty on grouping observations of non-zero response with those of zero response at the splitting procedure, and thus significantly enhance their separation. Finally, we examine and compare the predictive performance of such modified tree-based models to the traditional model on synthetic datasets that imitate insurance loss. The results show that such modification leads to substantially different tree structures and improved prediction performance.
@article{hu2022imbalanced,title={Imbalanced learning for insurance using modified loss functions in tree-based models},author={Hu, Changyue and Quan, Zhiyu and Chong, Wing Fung},journal={Insurance: Mathematics and Economics},volume={106},pages={13--32},year={2022},publisher={Elsevier},doi={10.1016/j.insmatheco.2022.04.010},url={https://doi.org/10.1016/j.insmatheco.2022.04.010},}
Improving Business Insurance Loss Models by Leveraging InsurTech Innovation
Zhiyu Quan, Changyue Hu, Panyi Dong, and 1 more author
Recent transformative and disruptive advancements in the insurance industry have embraced various InsurTech innovations. In particular, with the rapid progress in data science and computational capabilities, InsurTech is able to integrate a multitude of emerging data sources, shedding light on opportunities to enhance risk classification and claims management. This paper presents a groundbreaking effort as we combine real-life proprietary insurance claims information together with InsurTech data to enhance the loss model, a fundamental component of insurance companies’ risk management. Our study further utilizes various machine learning techniques to quantify the predictive improvement of the InsurTech-enhanced loss model over that of the insurance in-house. The quantification process provides a deeper understanding of the value of the InsurTech innovation and advocates potential risk factors that are unexplored in traditional insurance loss modeling. This study represents a successful undertaking of an academic-industry collaboration, suggesting an inspiring path for future partnerships between industry and academic institutions.
@misc{quan2024improving,title={Improving Business Insurance Loss Models by Leveraging InsurTech Innovation},year={2024},author={Quan, Zhiyu and Hu, Changyue and Dong, Panyi and Valdez, Emiliano A.},eprint={2401.16723},archiveprefix={arXiv},primaryclass={q-fin.RM},doi={10.48550/arXiv.2401.16723},url={https://doi.org/10.48550/arXiv.2401.16723},}
